Despite deep learning's widespread success, its data-hungry and computationally expensive nature makes it impractical for many data-constrained real-world applications. Few-Shot Learning (FSL) aims to address these limitations by enabling rapid adaptation to novel learning tasks, seeing significant growth in recent years. This survey provides a comprehensive overview of the field's latest advancements. Initially, FSL is formally defined, and its relationship with different learning fields is presented. A novel taxonomy is introduced, extending previously proposed ones, and real-world applications in classic and novel fields are described. Finally, recent trends shaping the field, outstanding challenges, and promising future research directions are discussed.

翻译：尽管深度学习取得了广泛成功，但其对数据的巨大需求和高计算成本使其在许多数据受限的实际应用中难以推广。小样本学习旨在通过实现对新型学习任务的快速适应来克服这些局限，近年来取得了显著进展。本文对领域最新进展进行了全面综述。首先正式定义小样本学习，并阐述其与不同学习领域的关系。提出一种新的分类体系，在先前分类基础上进行了扩展，并描述了经典和新领域中的实际应用。最后，讨论了当前塑造该领域的发展趋势、突出挑战以及有前景的未来研究方向。